Software Defect Backlog Forecasting via Validity Ratio Analysis
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Solution Overview
Problem
Current project planning methods for software development are imprecise in managing dynamically generated defect backlogs, often leading to over-commitment of resources and inefficient allocation, as they rely on ad hoc forecasts that fail to accurately predict the completion date or resource needs for resolving defects within the allotted time.
Innovation Solution
The method involves analyzing defect backlogs using a validity ratio and fix rate, combined with team performance data to estimate the date for resolving the backlog and the capacity to fix defects between specific dates, employing a six-state defect model and census data repositories to compute these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ad hoc forecasting methods are used to estimate defect backlog resolution, then resource allocation can be quickly adjusted, but the precision of completion date predictions deteriorates
Solution Approach 1:
The patent transforms the forecasting approach by changing key parameters from arbitrary ad hoc estimates to data-driven metrics including historical defect discovery rates, defect resolution rates, team velocity, and backlog size. These parameter changes enable precise completion date predictions while maintaining operational flexibility through dynamic recalculation as new data becomes available.
Solution Approach 2:
The system implements continuous feedback loops where actual defect discovery and resolution data are fed back into the forecasting model. This allows the completion date predictions to be continuously refined and updated as the project progresses, maintaining high precision while adapting to changing conditions without requiring complex replanning.
2Ease of manufacture
If predetermined percentage of budget is allocated for defect fixing, then resource allocation is simplified, but the accuracy of capacity forecasting deteriorates
Solution Approach 1:
The patent replaces the single parameter of predetermined budget percentage with multiple dynamic parameters including historical defect rates, team capacity metrics, and backlog characteristics. This transformation maintains allocation simplicity through automated calculations while dramatically improving capacity forecasting accuracy by considering multiple relevant factors simultaneously.
Solution Approach 2:
The system performs preliminary analysis of historical data and team capacity before final resource allocation decisions are made. By pre-calculating defect resolution capacity based on historical performance and current backlog characteristics, the system provides accurate capacity forecasts that guide resource allocation without requiring complex manual analysis at the time of decision-making.
3Productivity
If development resources are over-committed to draining defect backlog, then defect resolution capacity increases, but resources available for new code development decrease
Solution Approach 1:
The patent implements dynamic resource allocation where the split between defect resolution and new development resources is continuously adjusted based on real-time backlog size, severity, and projected completion dates. This dynamic approach ensures optimal resource distribution at any given time, preventing both over-commitment to defect fixing and under-utilization of development capacity.
Solution Approach 2:
The forecasting system provides continuous feedback on the trade-off between defect resolution progress and new development capacity. By monitoring actual versus predicted defect closure rates and their impact on new code delivery, the system enables informed decisions about resource reallocation, ensuring that defect fixing efforts do not unduly compromise new development productivity.
Data Source
AI summary
Methods, apparatus, and computer program products for analyzing defect backlogs that arise in the software development process. Analysis is based on a validity ratio that projects the number of open defects that are likely to actually require fixes, a fix rate that describes the performance of the development team charged with fixing the defects, defect census data, and team performance census data. One outcome of the analysis may be an estimate of the date by which the defect backlog should be resolved. Another outcome of the analysis may be an estimate of the capacity of a team to resolve defects between a given start date and a given target date.